Supervised self organizing maps with fuzzy error correction

ABSTRACT

A system and a method for an automated intelligent information mining includes receiving unstructured text from various text sources; extracting multiple key-phrases from the unstructured text; generating template and dynamic information contextual relation maps by mapping the extracted key-phrases to three-dimensional maps using a self organizing map, and a gaussian distribution technique. Further, the technique includes forming word clusters and constructing corresponding key-phrase frequency histograms for each of the generated contextual relation maps. Template and dynamic information three-dimensional structured document maps from the constructed phrase frequency histograms and the generated self-organizing maps. Desired information is extracted by mapping the generated dynamic information three-dimensional structured map onto the template three-dimensional structured map. A fuzzy prediction algorithm is used in possible error correction in extracting the desired intelligent information. A negative learning error correcting algorithm is used to correct the three-dimensional template relation maps depending on the fuzzy feedback.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to the co-pending, commonly assigned U.S.patent application Ser. No. 09/825,577, filed May 10, 2001, entitled“INDEXING OF KNOWLEDGE BASE IN MULTILAYER SELF-ORGANIZING MAPS WITHHESSIAN AND PERTURBATION INDUCED FAST LEARNING” is hereby incorporatedby reference in its entirety. This application is also related to theco-pending, commonly assigned U.S. patent application Ser. No.09/860,165, filed May 17, 2001, entitled “A NEURO/FUZZY HYBRID APPROACHTO CLUSTERING DATA” hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

This invention relates generally to the field of information mining, andmore particularly pertains to an automated intelligent informationmining technique.

BACKGROUND

With the explosive growth of available information sources it has becomeincreasingly necessary for users to utilize information miningtechniques to find, extract, filter, and evaluate desired information.Human translation is generally laborious, expensive, and error-prone andnot a feasible approach for extracting desired information.

Automating information mining techniques to mine information in textdocuments can be difficult because the text documents are in humanreadable and understandable format that lack inherently definedstructure and appears as meaningless data for the information miningtechniques, because text can come from various sources, such as adatabase, e-mail, Internet and/or through a telephone in differentforms. Also, text documents coming from various sources can be highdimensional in nature containing syntactic, semantic (contextual)structure of words/phrases, temporal and spatial information which cancause disorderliness in the information mining process.

Current information mining techniques such as hierarchical keywordsearches, statistical and probabilistic techniques, and summarizationusing linguistic processing, clustering, and indexing dominate theunstructured text processing arena. The most prominent and successful ofthe current information mining techniques require huge databasesincluding domain specific keywords, comprehensive domain specificthesauruses, computationally intensive processing techniques, laborioushuman interface and human expertise.

There has been a trend in the development of information miningtechniques to be domain independent, to be adaptive in nature, and to beable to exploit contextual information present in text documents toimprove processing speeds of information mining techniques. Currenttechniques for information mining use self-organizing maps (SOMs) toexploit the contextual information present in the text. Currently, SOMsare the most popular artificial neural network algorithms. SOMs belongto a category of competitive learning networks. SOMs are generally basedon unsupervised learning (training without a teacher), and they providea topology that preserves contextual information of unstructureddocument by mapping from a high dimensional data (unstructured document)to a two dimensional map (structured document), also called map units.Map units, or neurons, usually form a two dimensional grid and hence themapping from high dimensional space onto a plane. Thus, SOMs serve as atool to make clusters for analyzing high dimensional data. Word categorymaps are SOMs that have been organized according to word similarities,measured by the similarity between short contexts of the words.Contextually interrelated words tend to fall into the same orneighboring map nodes. Nodes may thus be viewed as word categories.

Current pending U.S. patent application Ser. No. 09/825,577, dated May10, 2002, entitled “INDEXING OF KNOWLEDGE BASE IN MULTILAYERSELF-ORGANIZING MAPS WITH HESSIAN AND PERTURBATION INDUCED FASTLEARNING” discloses such an information technique using the SOMs that isdomain independent, adaptive in nature that can exploit contextualinformation present in the text documents, and can have an improvedlearning rate that does not suffer from losing short contextualinformation. One drawback with this technique is that the histogramformed from the clusters is very much dependent on the clusters and isvery specific and sensitive to the cluster boundary. The elements in ornear the boundary may suffer from this rigidity. This might have adverseeffects on the accuracy of the information mining.

The SOM based algorithm disclosed in the above-mentioned pendingapplication uses heuristic procedures and so termination is not based onoptimizing any model of the process or its data. The final weightvectors used in the algorithm usually depend on the input sequence.Different initial conditions yield different results. It is recommendedthat the alteration of several parameters of the self-organizingalgorithm, such as learning rate, the size of update neighborhood andthe strategy to alter these parameters during learning from one data setto another will yield useful results. There is a need for an improvedadaptive algorithm responsive to changing scenarios and external inputs.There is a further need for uniformity in neighborhood size. There isyet a further need for an algorithm that preserves neighborhoodrelationships of the input space in the face of bordering neurons thathave fewer neighborhoods than others.

SUMMARY OF THE INVENTION

The present invention provides an automated intelligent informationmining technique for various types of information mining applicationssuch as data and text mining applications, identification of a signalfrom a stream of signals, pattern recognition applications, and/ornatural language processing applications. Unstructured text is receivedfrom various text sources, and key-phrases are extracted from thereceived unstructured text. Each of the extracted key-phrases aretransformed into a unique numerical representation. Layers of templateand dynamic information contextual relation maps are generated bymapping the transformed key-phrases to the surfaces of three-dimensionalmaps, respectively, using a self-organizing map and a gaussiandistribution (function approximation of neighborhood). Further, wordclusters are formed and corresponding key-phrase frequency histogramsare constructed for each of the generated contextual relation maps.Template and dynamic information three-dimensional structured documentmaps are generated from the constructed key-phrase frequency maps andthe generated contextual maps using the self-organizing map and gaussiandistribution technique. Desired information is extracted by masking thegenerated dynamic information three-dimensional structured map to thetemplate three-dimensional structured map.

If the extracted information is not substantially the same as theexpected information, a fuzzy prediction algorithm using basis histogramon the histograms obtained from the template and dynamic informationcontextual relation maps is used to extract desired information. Theextracted desired intelligent information obtained using the fuzzyprediction algorithm is also compared to the expected information. Alearning vector quantization (LVQ) based negative learning errorcorrecting algorithm is used to correct the formed 3D templateinformation structured map, when the extracted information obtainedusing the fuzzy prediction algorithm is substantially same as theexpected information.

The LVQ based negative learning error correcting algorithm is used tocorrect the three-dimensional template contextual relation map, when theextracted desired intelligent information obtained using the fuzzyprediction algorithm is not substantially same as the expectedinformation.

Other aspects of the invention will be apparent on reading the followingdetailed description of the invention and viewing the drawings that forma part thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating one embodiment of intelligentinformation mining according to the present invention.

FIG. 2 is a flowchart illustrating one embodiment of a closed loopsystem including an error feedback function used to extract intelligentinformation mining according to the present invention.

FIG. 3 is a flowchart illustrating one embodiment of using a clusteringalgorithm in intelligent information mining according to the presentinvention.

FIG. 4 is a schematic diagram illustrating the formation and of templateand dynamic information three-dimensional contextual SOMs and mapping ofthe formed contextual SOMs to obtain desired intelligent information.

FIG. 5 is an overview illustrating one embodiment of acomputer-implemented system according to the present invention.

FIG. 6 shows an example of a suitable computing system environment forimplementing embodiments of the present invention, such as those shownin FIGS. 1, 2, 3, 4, and 5.

DETAILED DESCRIPTION

This document describes an improved automated information miningtechnique applicable to various types of information mining applicationssuch as data and text mining applications, identification of a signalfrom a stream of signals, pattern recognition applications, and/ornatural language processing applications.

FIG. 1 is a flowchart illustrating one example embodiment of a process100 of intelligent information mining of the present invention. Theflowchart includes operations 110–180, which are arranged serially inthe exemplary embodiment. However, other embodiments of the inventionmay execute two or more operations in parallel using multiple processorsor a single processor organized as two or more virtual machines orsub-processors. Moreover, still other embodiments implement theoperations as two or more specific interconnected hardware modules withrelated control and data signals communicated between and through themodules, or as portions of an application specific integrated circuit.Thus, the exemplary process flow is applicable to software, firmware,and hardware implementations.

The process begins with operation 110 by receiving unstructured textfrom various sources such as a data base/data warehouse, a LAN/WANnetwork, SAN, Internet, a voice recognition system, and/or amobile/fixed phone. Operation 110 can also begin by receiving imagesignals that are stored in a buffer, online, and/or a file.

Operation 110 further includes extracting multiple key-phrases from thereceived unstructured text. In some embodiments, element 110 alsoextracts multiple key-words from the received text and can form themultiple key-phrases from the extracted key-words. In these embodiments,element 110 extracts key-words from the received text based on aspecific criteria such as filtering to remove all words comprised ofthree or fewer letters, and/or filtering to remove rarely used words.The formed key-phrases can include one or more extracted key-words andany associated preceding and following words adjacent to the extractedkey-words to include contextual information. In some embodiments,element 110 further morphologizes the extracted key-words based onfundamental characteristics of the extracted key-words. For example, theelement 110 can morphologize in such a way that morphed (altered) words'pronunciation or meaning remain in place.

Operation 120 transforms each of the extracted key-words, phrases and/ormorphed words to a unique numerical representation. Extracted key-wordsare transformed such that the transformed unique numericalrepresentation does not result in multiple similar numericalrepresentations, to avoid ambiguous prediction of meaning of thetranslated words in the received text.

Operation 130 generates a layer of three-dimensional (3D) templatecontextual relation map using a self-organizing map (SOM) to categorizethe extracted key-phrases based on contextual meaning. In someembodiments, the layer of 3D template contextual relation map isgenerated by obtaining a predetermined amount of key-phrases from theextracted multiple key-phrases. In some embodiments, the 3D templatecontextual relation map is a spherical shaped template contextualrelation map.

Before proceeding with generating of the 3D template contextual relationmap, the map parameters are set to naturally converge around a sphere byconsidering each row of neurons in the map to represent a horizontalslice of a sphere with the angle of latitude between adjacent slicesbeing equal. The number of neurons n_(k) in the slice k is proportionalto the circumference of the slice. The following equation is used tocalculate the number of neurons n_(k) given d, the number of slices,n _(k)=2d sin(π/2−θ_(n))Where

-   -   θ_(n): the angle of latitude of slice n.

The resulting map will be the acceptable shape to converge to thetopology of a sphere.

Input patterns (multiple key-phrases) are then presented to theself-organizing map (artificial neural network).x₁, x₂, . . . x_(n)εR^(n)

where each of the x₁, x₂, . . . x_(n) are triplets (normalized uniquerepresentations of key-phrases including preceding word, reference keyword, and succeeding word) of the high dimensional text data

Random weights are then initialized using a random number generator andnormalized between 0 and 1 (because the inputs to the network are alsonormalized between 0 and 1). The strength between input and output layernodes are referred to as weights, and updating of weights is generallycalled learning.

w_(i)≡[w_(i1), w_(i2), . . . , w_(in)]^(T)εR^(n), where w_(i1), w_(i2),. . . , w_(in) are the random weights, where ‘n’ is a dimension of theinput layer. Generally, ‘n’ is based on the number of inputpatterns/vectors (key-phrases). In the following example of assignedrandom weights, dimension ‘n’ is initialized using 10.

0.24, 0.98, 0.47, . . . , 0.25, 0.94, 0.62

In some embodiments, the initial neighborhood radius is set to σ₀=π/6,the initial neighborhood is taken as the circle with radius σ₀.

Compute distance to all nodes using modality-vectors as follows:d _(1j)−cos⁻¹[(x ₁ x _(j) +y ₁ y _(j) +z ₁ z _(j))/(√(x ₁ ² +y ₁ ² +z ₁²)*√(x _(j) ² +y _(j) ² +z _(j) ²))] 0.6239, 0.81. 0.04 . . .

The winner among all the nodes are then determined as follows:

d_( i, c) = (min {d_( i, j)})  ∀j:  1  to  m      = 0.04.

update the value of the weight vector of the winner and neighborhoodusing the following equation:w _(j)(n+1)=w _(j)(n)+η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]

Wherein W_(j)=weights of node j

X(n)=input at time n

πj,i (n)=neighborhood function centered around winning node I(x) givenbyexp(−d ² _(1j)/2σ²(n))Wherein η(n)=the learning rate with typical range [0.1–0.01]η₀exp(−n/τ ₂)σ(n)=Standard deviationσ₀exp (−n/τ₁)σ₀=3.14/6Wherein τ₁, τ_(n)=time constantsτ₁=1000/log (σ₀), τ_(n)=1000

The angle subtended by categories (neurons) at the center of the spherecan be taken as measure of topographic distance between two neurons. Iftwo neurons are spatially located at (x1, y₁, z₁) and (x2, y₂, z₂) thenangular distance is given byd _(1j)=cos⁻¹[(x ₁ x ₂ +y ₁ y ₂ +z ₁ z ₂)/(√(x ₁ ² +y ₁ ² +z ₁ ²)*√(x ₂² +y ₂ ² +z ₂ ²))]

Operation 140 includes generating a layer of 3D dynamic informationcontextual map using the extracted multiple key-phrases. The processused to generate the layer of 3D dynamic information contextual relationmap is similar to the above-described process of generating the layer of3D template contextual relation map. In some embodiments, the 3D dynamicinformation contextual map is a spherical shaped 3D dynamic informationcontextual map. Operations 130 and 140 are performed in parallel in oneembodiment, but may also be performed serially.

Operation 150 includes forming phrase clusters for the generatedtemplate and dynamic information contextual relation maps. In someembodiments, the phrase clusters are formed based on positions obtainedfrom the above-illustrated equations using the least square erroralgorithm.

Operation 160 includes constructing key-phrase frequency histogramconsisting of frequency of occurrences of multiple key-phrases using thegenerated template and dynamic information contextual relation maps. Insome embodiments, the key-phrase frequency histogram is constructed bydetermining the number of times each of the key-phrases appear, in eachof the generated contextual relation maps.

Operation 170 then includes generating template and dynamic informationthree-dimensional (3D) structured document maps using the constructedphrase frequency histogram and the generated contextual relation mapsusing the self-organizing map so that each of the generated 3Dstructured document maps include phrase clusters based on similarityrelationship between the formed word clusters. Operation 180 thenobtains desired intelligent information by mapping the generated 3Ddynamic information structured map to the template 3D structured map.

In some embodiments, the template and dynamic information contextualrelation maps and the template and dynamic information structured mapsare generated by mapping the transformed multiple key-phrases on to thesurface of the spherical map using the self-organizing map and thegaussian approximation neighborhood technique. The gaussian distributionenables the neighbor neurons selected for weight updation to beindependent of the neighborhood structure. In these embodiments,gaussian approximation neighborhood technique includes updating valuesof weight factors of winner category and neighborhood using theequation:w _(j)(n+1)=w _(j)(n)−η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]w _(j)(n+1)=w _(j)(n)+η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]

Wherein w_(j)=weights of node j , X(n)=input at time n, and πj,i(n)=neighborhood function centered around winning node I(x) is given bythe gaussian distribution function:exp(−d ² _(1J)/2σ²(n))

Wherein η(n)=the learning rate with typical range [0.1–0.01], η₀exp(−n/τ₂), σ(n)=Standard deviation σ₀exp(−n/τ₁), σ₀=3.14/6, and τ₁,τ_(n)=time constants, where τ₁=1000/log (σ₀) and τ_(n)=1000.

FIG. 2 is a flowchart illustrating one example embodiment of a process200 of error correction algorithm used to correct boundary sensitivenessaccording the present invention. The flowchart includes operations210–250 Other embodiments of the invention may execute operationsserially, or two or more operations in parallel using multipleprocessors or a single processor organized as two or more virtualmachines or sub-processors. Moreover, still other embodiments implementthe operations as two or more specific interconnected hardware moduleswith related control and data signals communicated between and throughthe modules, or as portions of an application specific integratedcircuit. Thus, the exemplary process flow is applicable to software,firmware, and hardware implementations.

The process begins with extracting desired intelligent information fromunstructured text using the 3D template contextual map and the 3Dtemplate structured information map at 210 as described-above withreference to FIG. 1. Error feedback from future operations is provided.Operation 220 compares the extracted desired intelligent information toexpected information. Operation 222 includes stopping the process 200and keeping the extracted desired intelligent information, when theextracted desired intelligent information is substantially same as theexpected information.

If the extracted information is not substantially the same as theexpected information, operation 230 applies a fuzzy prediction algorithmusing basis histogram on the histograms obtained from the template anddynamic information contextual relation maps to extract desiredintelligent information. One such fuzzy prediction algorithm isdescribed in U.S. patent application Ser. No. 09/860,165, filed May 17,2001, entitled “A NEURO/FUZZY HYBRID APPROACH TO CLUSTERING DATA” herebyincorporated by reference in its entirety. Operation 240 compares theextracted desired intelligent information obtained using the fuzzyprediction algorithm to the expected information. Operation 242 includesapplying a learning vector quantization (LVQ) based negative learningerror correcting algorithm to correct the formed 3D template informationstructured map, when the extracted desired intelligent information issubstantially same as the expected information.

Operation 250 includes applying the LVQ based negative learning errorcorrecting algorithm to correct the 3D template contextual relation map,when the extracted desired intelligent information obtained using thefuzzy prediction algorithm is not substantially same as the expectedinformation. The information extraction continues using the corrected 3Dself organizing maps.

In some embodiments, applying the LVQ based negative learning errorcorrecting algorithm includes applying substantially small negative andpositive learning correction to an outer cover to correct and incorrectcluster boundaries in the 3D template structured map and the 3D templatecontextual relation map using the equation:w _(j)(n+1)=w _(j)(n)−η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]w _(j)(n+1)=w _(j)(n)+η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]

Wherein w_(j)=weights of node j , X(n)=input at time n, and πj,i(n)=neighborhood function centered around winning node I(x).

Operations 205–250 are repeated until the extracted desired intelligentinformation is substantially same as the expected information.

FIG. 3 is a flowchart illustrating one example embodiment of a process300 of cluster formation according to the present invention. Theflowchart includes operations 310–360, which are arranged serially inthe exemplary embodiment. However, other embodiments of the inventionmay execute two or more operations in parallel using multiple processorsor a single processor organized as two or more virtual machines orsub-processors. Moreover, still other embodiments implement theoperations as two or more specific interconnected hardware modules withrelated control and data signals communicated between and through themodules, or as portions of an application specific integrated circuit.Thus, the exemplary process flow is applicable to software, firmware,and hardware implementations.

The process begins with operation 310 by calculating a cumulativefrequency for each category mapped to a cell in the 3D template anddynamic information contextual relation maps. Operation 320 includescalculating a goodness factor for each calculated cumulative frequency.In some embodiments, goodness factor each category C_(i), w.r.t eachcell j is calculated using the equation:G(C _(i) , j)=F ^(Clust)(C _(i))/F ^(Coll)(C _(i))

wherein F^(Cell) is the category C_(i) in relation to other categoriesin the cell j, F^(Coll) relates to category C_(i) to the wholecollection, wherein

${G\left( {C_{i},j} \right)} = \frac{{F_{j}\left( C_{i} \right)}*{F_{j}\left( C_{i} \right)}}{{{F_{J}\left( C_{i} \right)} + \sum\limits_{i}} ⊄ {A_{I}^{j}{F_{j}\left( C_{i} \right)}}}$

wherein i⊂A_(l) ^(j) if d (i,j)<(r₁=the radius of the neutral zone),r₁=3*d_(i,j, i,j) being adjacent andF_(j)(C_(i))=f_(j)(C_(i))/Σ_(j)f_(j)(C_(i))−the relative frequency ofcategory C_(i), wherein f_(j)(C_(i))=the frequency of category C_(i) inj.

Operation 330 includes labeling each category mapped to a cell in the 3Dtemplate and 3D dynamic information based on the calculated goodnessfactor. Operation 340 includes clustering the labeled categories byapplying least mean square error clustering algorithm to each of thecategories.

Operation 350 then includes comparing each category using the followingequation:index(min(d_(m,cluster centers)))∈(i,j)

Operation 350 includes stopping the process 300 if the above conditionis not true otherwise operation 360 includes merging the clusteredcategories based on the labels. In some embodiments, clusters are mergedby finding midpoint (m) between the centers of clusters (I,j). Distancefrom m to all cluster centers is then determined. The above equation isthen used to merge the clusters.

FIG. 4 and the following example including unstructured text includingfault information received from aircraft maintenance manuals furtherillustrate the process of information mining technique employed by thepresent invention:

Category 1

PREREQUISITES MAKE SURE THESE SYSTEMS WILL OPERATE: AIR/GROUND SYSTEM(AMM 32-09-02 /201). MAKE SURE THE AIRPLANE IS IN THIS CONFIGURATION:ELECTRICAL POWER (AMM 24-22-00 /201)

Category 2

DO THE ANTENNA AND CABLE CHECK PROCEDURE FOR THE APPLICABLE LEFTCENTER,RIGHT) ILS RECEIVER (AMM 34-00-00/201 ). PERFORM PSEU BITEPROCEDURE (FIM 32-09-03, FIG. 103, BLOCK 7 ACTION).

Category 3

L OR R SYSTEM FAULTS, REPLACE THE PRESSURE SWITCH, S25 (S30), FOR THEALTERNATING CURRENT MOTOR PUMP (ACMP) IN THE LEFT (RIGHT) HYDRAULICSYSTEM (AMM 29-11-18 /401 ). C SYSTEM FAULTS, ADJUST THE PRESSURESWITCH, S10003 (S10016), FOR THE ALTERNATING CURRENT MOTOR PUMP (ACMP)C1 (C2) IN THE CENTER HYDRAULIC SYSTEM (AMM 29-11-19 /401).

Category 4

EXAMINE AND RREPAIR THE CIRCUIT BETWEEN THE FCC CONNECTOR D381A, PIN K3AND TB127, PIN G43 (WDM 22-15-12.). CHECK FOR CONTINUITY BETWEEN PINS A7AND A8 OF CONNECTOR D2712A, (WDM 21-31-21. ).

After completing the operations 110 and 120 described-above withreference to FIG. 1, the following key-words, key-phrases, and uniquenumerical representations are obtained for each received category ofunstructured text:

Word, Code & Winner Nodes for Category 1

PREREQUISITES (0.027631 0.030854 0.024407) *22* MAKE (0.030854 0.0244070.036636) *22* SURE (0.024407 0.036636 0.037852) *22* THESE (0.0366360.037852 0.036835) *22* SYSTEMS (0.037852 0.036835 0.043527) *22* WILL(0.036835 0.043527 0.028883) *22* OPERATE: (0.043527 0.028883 0.002341)*22* AIR/GROUND (0.028883 0.002341 0.036835) *22* SYSTEM (0.0023410.036835 0.000068) *22* AMM (0.036835 0.000068 0.018451) *22* MAKE(0.030521 0.024407 0.036636) *22* SURE (0.024407 0.036636 0.002341) *22*AIRPLANE (0.036636 0.002341 0.000013) *22* IN (0.002341 0.0000130.037857) *22* THIS (0.000013 0.037857 0.006376) *22* CONFIGURATION:(0.037857 0.006376 0.009961) *22* ELECTRICAL (0.006376 0.0099610.030730) *22* POWER (0.009961 0.030730 0.000068) *22* AMM (0.0307300.000068 0.015399) *22*

Word, Code & Winner Nodes for Category 2

DO (0.251298 0.250007 0.252589) *61* ANTENNA (0.250007 0.2525890.255671) *61* CABLE (0.252589 0.255671 0.256019) *61* CHECK (0.2556710.256019 0.280867) *61* PROCEDURE (0.256019 0.280867 0.250317) *71* FOR(0.280867 0.250317 0.252683) *61* APPLICABLE (0.250317 0.2526830.272725) *61* LEFT (0.252683 0.272725 0.250155) *61* (CENTER,RIGHT)(0.272725 0.250155 0.250461) *61* ILS (0.250155 0.250461 0.283956) *61*RECEIVER (0.250461 0.283956 0.250068) *71* AMM (0.283956 0.2500680.267012) *61* PERFORM (0.280567 0.280230 0.280904) *7* PSEU (0.2802300.280904 0.280904) *7* PSEU (0.280904 0.280904 0.254216) *7* BITE(0.280904 0.254216 0.250001) *5* E (0.254216 0.250001 0.280867) *7*PROCEDURE (0.250001 0.280867 0.250309) *7* FIM (0.280867 0.2503090.261688) *5* FIG. (0.250309 0.261688 0.254357) *7* BLOCK (0.2616880.254357 0.252048) *7* ACTION (0.254357 0.252048 0.253202) *7*

Word, Code & Winner Nodes for Category 3

L (0.500011 0.500001 0.500021) *26* OR (0.500001 0.500021 0.500001) *26*R (0.500021 0.500001 0.536835) *26* SYSTEM (0.500001 0.536835 0.511313)*10* FAULTS, (0.536835 0.511313 0.533973) *10* REPLACE (0.5113130.533973 0.530854) *10* PRESSURE (0.533973 0.530854 0.536723) *10*SWITCH, (0.530854 0.536723 0.500317) *10* FOR (0.5367230.5003170.502491) *26* ALTERNATING (0.500317 0.502491 0.506677) *26*CURRENT (0.502491 0.506677 0.525109) *26* MOTOR (0.506677 0.5251090.531013) *10* PUMP (0.525109 0.531013 0.500054) *10* (ACMP) (0.5310130.500054 0.500013) *26* IN (0.500054 0.500013 0.522725) *26* LEFT(0.500013 0.522725 0.500899) *26* (RIGHT) (0.522725 0.500899 0.516218)*26* HYDRAULIC (0.500899 0.516218 0.536835) *26* SYSTEM (0.5162180.536835 0.500068) *10* AMM (0.536835 0.500068 0.518451) *26*

C (0.518418 0.500001 0.536835) *26* SYSTEM (0.500001 0.536835 0.511313)*84* FAULTS, (0.536835 0.511313 0.502084) *26* ADJUST (0.511313 0.5020840.530854) *26* PRESSURE (0.502084 0.530854 0.536723) *26* SWITCH,(0.530854 0.536723 0.500317) *13* FOR (0.536723 0.500317 0.502491) *26*ALTER (0.500317 0.502491 0.526291) *26* NATING (0.502491 0.5262910.506677) *11* CURRENT (0.526291 0.506677 0.525109) *26* MOTOR (0.5066770.525109 0.531013) *26* PUMP (0.525109 0.531013 0.500054) *26* (ACMP)(0.531013 0.500054 0.500013) *26* IN (0.500054 0.500013 0.505884) *10*CENTER (0.500013 0.505884 0.516218) *10* HYDRAULIC (0.505884 0.5162180.536835) *26* SYSTEM (0.516218 0.536835 0.500068) *11* AMM (0.5368350.500068 0.518451) *26*

Word, Code & Winner Nodes for Category 4

EXAMINE (0.772573 0.760548 0.784599) *79* RREPAIR (0.760548 0.7845990.756085) *79* *79* BETWEEN (0.756085 0.754019 0.750301) *79* FCC(0.754019 0.750301 0.756376) *79* CONNECTOR (0.750301 0.756376 0.750802)*79* PIN (0.756376 0.750802 0.750802) *79* PIN (0.750802 0.7508020.751140) *79* WDM (0.750802 0.751140 0.750971) *79* CHECK (0.7531680.756019 0.750317) *79* FOR (0.756019 0.750317 0.756376) *79* CONTINUITY(0.750317 0.756376 0.754019) *79* BETWEEN (0.756376 0.754019 0.780423)*79* PINS (0.754019 0.780423 0.750021) *79* OF (0.780423 0.7500210.756376) *79* CONNECTOR (0.750021 0.756376 0.751140) *79* WDM (0.7563760.751140 0.753758) *79*

FIG. 4 illustrates the formation of a 3D template and dynamic contextualrelation map and template and dynamic 3D structured map using apredetermined number of key-phrases from above transformed key-phrases.It further more illustrates the mapping of the formed 3D dynamicinformation structured map on to the template 3D structured map toobtain the desired intelligent information.

FIG. 4 and following example further illustrates computing a goodnessfactor for each category in the template and dynamic information 3Dcontextual maps. Further, the following example illustrates labeling,clustering, and merging the categories based on the computed goodnessfactor.

FIG. 4 is only an illustration of the concept. The numbers in the mapshown need not be considered. The following example illustrates theactual computation, where the numbers are important.

Also, for further illustration, the categories such as:

0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 00 0 0 0 0 0represent a document vector. This vector gets mapped to a single cell inthe 3D SOM map. In this way, FIG. 4, and the following example do notrelate to the same entities.

SOM 1 Labeling

Labels Generated for each SOM 1 Elements is shown in figure For left topelement in figure label is calculated as follows

-   -   <1 2 19 0 *2*> Words mapped from first Category: 1    -   Similarly from second ,third and fourth are 2 ,19 & 0        respectively.    -   The Local goodness for category three label is calculated as        follows        F ^(local)=1/(1+2+19+0) F ^(local)=2(1+2+19+0) F        ^(local)=19/(1+2+19+0) F ^(local)=19/(1+2+19+0)=0.8632    -   Global goodness for third category is        F ^(Global)=0.8632/(0.8632+0.3250+0+0.57676+ . . .        +0.7250)=0.181 Neighboring 3 elements excluded.    -   Effective Goodness=0.181*0.8632=0.1563    -   For categories goodnesses found to be 0.0012, 0.0019,0.0 (first        second & fourth respectively)    -   Therefore category label assigned for element is 3.

SOM 1 Clustering

-   -   21 Clusters obtained.    -   Eg. CLUSTER 1 Center→0.053578 0.470783 0.853184 Radius→0.785714    -   Element Coordinates −0.083032 0.325694 0.941822    -   Category Label *0*    -   Element Coordinates 0.190189 0.615872 0.764546    -   Category Label *0*

Following illustrates the formation of basis histograms obtained bytraining and using template 3D contextual relation and structured mapsshown in FIG. 4.

For Category 1 sentences: 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 For Category 2 sentences 0 0 2 0 00 0 0 0 7 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 8 4 0 0 0 0 0 0 0 0 00 For Category 3 sentences 0 3 0 0 2 2 0 0 4 0 0 0 7 0 0 0 0 0 0 0 0 0 10 1 2 2 0 0 3 0 0 0 5 0 0 0 0 0 0 0 0 For Category 4 sentences 0 0 0 0 00 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0 0 00

Following illustrates the Fuzzy prediction by inputting the above basishistograms extracted from each category in the process of training:

Category 1 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 {close oversizebrace} Lower bound for the basis histogram 0 0 0 0 0 0 0 0 0 7 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 {close oversizebrace} Average between lower and upper bounds 0 0 0 0 0 0 0 0 0 11 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 } Upper boundfor the basis histogram Category 2 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 00 0 {close oversize brace} Lower bound for the basis histogram 0 0 1 0 00 0 0 0 5 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 7 3 0 0 0 0 0 0 0 0 00 {close oversize brace} Average between lower and upper bounds 0 0 1 00 0 0 0 0 8 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 1 0 0 0 0 0 0 0 00 0 {close oversize brace} Upper bound for the basis histogram 0 0 4 0 00 0 0 0 12 4 0 0 0 0 0 0 0 0 0 0 Category 3 0 1 0 0 1 0 0 0 2 0 0 0 1 00 0 0 0 0 0 0 {close oversize brace} Lower bound for the basis histogram0 2 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 3 4 0 0 3 0 0 0 5 0 00 0 0 0 0 0 {close oversize brace} Average between lower and upperbounds 0 2 0 0 3 1 0 0 3 0 0 0 5 0 0 0 0 0 0 0 0 0 3 0 0 2 2 0 0 4 0 0 07 0 0 0 0 0 0 0 0 {close oversize brace} Upper bound for the basishistogram 0 4 0 0 4 1 0 0 3 0 0 0 6 0 0 0 0 0 0 0 0 Category 4 0 0 0 0 00 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 } Lower bound for the basis histogram 00 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 {close oversize brace} Averagebetween lower and upper bounds 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 {close oversize brace}Upper bound for the basis histogram 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 00 0 0

Input to Fuzzy prediction is 0 0 0 0 3 0 0 0 3 0 0 0 1 0 0 0 0 0 0 0 02. Correct classification is category 2. Classification obtaining by 3Ddynamic information structured map is 1. Classification obtained usingfuzzy prediction is category 2. Therefore, LVQ based negative learningis applied to the 3D template information structured map.

FIG. 5 illustrates an overview of one embodiment of acomputer-implemented system 500 according to the present invention. Aweb server 520 is connected to receive unstructured text from varioussources 510A, 510B, 510C and 510D. For example, the web server 520 canreceive unstructured text from sources, such as a data base/datawarehouse, a LAN/WAN network, SAN (Storage Area Networks) Internet, avoice recognition system, and/or a telephone. In some embodiments, theunstructured text can be product-related text that can come from sourcessuch as product manuals, maintenance manuals, and/or answers tofrequently asked questions (FAQs). The received text can be in anynatural language.

The computer-implemented system 500 includes a key-word/phrase extractor530. The key-word/phrase extractor 530 is connected to the web server520 and extracts multiple key-phrases from the received text. In someembodiments, the key-word/phrase extractor 530 can also extract multiplekey-words from the received text and can form the multiple key-phrasesfrom the extracted key-words. In some embodiments, the keyword/phraseextractor 530 extracts key-words from the received text based onspecific criteria such as filtering to remove all words comprising threeor fewer letters, filtering to remove general words, and/or filtering toremove rarely used words. The formed key-phrases can include one or moreextracted key words and any associated preceding and succeeding(following) words adjacent to the extracted key words to includecontextual information. In some embodiments, the key-word/phraseextractor 530 can further morphologize the extracted key-words based onfundamental characteristics of the extracted key-words. For example, thekey-word/phrase extractor 530 can morphologize in such a way thatmorphed (altered) words' pronunciation or meaning remain in place.

An analyzer 540 is coupled to the key-word/phrase extractor 530transforms each of the extracted product-related information and querykey-words, phrases and/or morphed words to a unique numericalrepresentation such that the transformed unique numerical representationdoes not result in multiple similar numerical representations, to avoidambiguous prediction of meaning of the translated words in the receivedtext. Analyzer 540 also performs the three dimensional mapping andclassification as described above.

Block 550 represents an interface for communicating desired informationgenerated by system 500. In some embodiments, block 550 provides theinformation to a display for display to a user. In further embodiments,block 550 provides the information via a network to another system onthe network, or simply stores the information in local or remote storagefor later use.

FIG. 6 shows an example of a suitable computing system environment 600for implementing embodiments of the present invention, such as thoseshown in FIGS. 1, 2, 3, 4, and 5. Various aspects of the presentinvention are implemented in software, which may be run in theenvironment shown in FIG. 6 or any other suitable computing environment.The present invention is operable in a number of other general purposeor special purpose computing environments. Some computing environmentsare personal computers, server computers, hand-held devices, laptopdevices, multiprocessors, microprocessors, set-top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments, and the like. The present inventionmay be implemented in part or in whole as computer-executableinstructions, such as program modules that are executed by a computer.Generally, program modules include routines, programs, objects,components, data structures and the like to perform particular tasks orto implement particular abstract data types. In a distributed computingenvironment, program modules may be located in local or remote storagedevices.

FIG. 6 shows a general computing device in the form of a computer 610,which may include a processing unit 602, memory 604, removable storage612, and non-removable storage 614. Memory 604 may include volatilememory 606 and nonvolatile memory 608. Computer 610 may include—or haveaccess to a computing environment that includes—a variety ofcomputer-readable media, such as volatile memory 606 and non-volatilememory 608, removable storage 612 and non-removable storage 614.Computer storage includes RAM, ROM, EPROM & EEPROM, flash memory orother memory technologies, CD ROM, Digital Versatile Disks (DVD) orother optical disk storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumcapable of storing computer-readable instructions. Computer 610 mayinclude or have access to a computing environment that includes input616, output 618, and a communication connection 620. The computer mayoperate in a networked environment using a communication connection toconnect to one or more remote computers. The remote computer may includea personal computer, server, router, network PC, a peer device or othercommon network node, or the like. The communication connection mayinclude a Local Area Network (LAN), a Wide Area Network (WAN) or othernetworks.

Computer-readable instructions stored on a computer-readable medium areexecutable by the processing unit 602 of the computer 610. A hard drive,CD-ROM, and RAM are some examples of articles including acomputer-readable medium. For example, a computer program 625 capable ofextracting desired intelligent information from unstructured textaccording to the teachings of the present invention may be included on aCD-ROM and loaded from the CD-ROM to a hard drive. The computer-readableinstructions allow computer system 610 to provide generic accesscontrols in a COM-based computer network system having multiple clientsand servers.

CONCLUSION

The above-described computer-implemented technique provides, among otherthings, a method and apparatus for an intelligent information miningthat can be domain independent, that can adapt in nature, that canexploit contextual information present in the text documents. Inaddition, the technique describes a closed loop system including anerror feedback function to reduce clustering errors and cluster boundarysensitivity. The spherical SOM map described with respect to someembodiments is one illustration of the invention. Further embodimentsutilized a generalized n dimensional SOM map.

1. An information mining method, comprising: extracting multiplekey-phrases from unstructured text; obtaining a predetermined number ofkey-phrases from the multiple key-phrases; generating a layer oftemplate contextual relation map by mapping the predetermined number ofkey-phrases to a three-dimensional map using a self-organizing map;generating a layer of dynamic information contextual relation map forthe received unstructured text by mapping the transformed key-phrases tothe three-dimensional map using the self-organizing map; forming phraseclusters for the template contextual relation map; mapping phrases ofthe information to be classified using the phrase clusters of thegenerated template contextual relation map; constructing template anddynamic information key-phrase frequency histograms consisting of thefrequency of occurrences of key-phrases, respectively, from thegenerated template contextual relation map and the dynamic informationcontextual relation map; generating template and dynamic informationthree-dimensional structured maps from each of corresponding templateand dynamic key-phrase frequency histograms; and extracting desiredinformation by mapping the dynamic information three-dimensionalstructured map on to the template three-dimensional structured map. 2.The method of claim 1, further comprising: receiving unstructured textfrom various text sources, wherein the text sources are selected fromthe group comprising of product manuals, maintenance manuals, and anydocuments including unstructured text.
 3. The method of claim 1, furthercomprising: extracting multiple key-phrases from the unstructured textsources; and forming the multiple key-phrases from each of the extractedmultiple key-phrases.
 4. The method of claim 3, wherein extractingmultiple key phrases comprises: extracting multiple key-phrases from theunstructured text sources based on specific criteria selected from thegroup comprising filtering to removing all words comprising three orfewer letters, filtering to remove general words, and filtering toremove rarely used words.
 5. The method of claim 3, wherein thekey-phrases comprise: one or more key-words and/or one or morekey-phrases.
 6. The method of claim 3, wherein key-phrases comprise: oneor more extracted key-phrases and associated preceding and followingwords adjacent to the extracted key-phrases to include contextualinformation.
 7. The method of claim 1, further comprising: transformingeach of the extracted key-phrases into a unique numericalrepresentation.
 8. An intelligent information mining method, comprising:receiving unstructured text; extracting multiple key-phrases from theunstructured text; transforming each of the extracted key-phrases into aunique numerical representation; obtaining a predetermined number ofkey-phrases from the transformed key-phrases; generating a layer oftemplate contextual relation map by mapping the predetermined number ofkey-phrases on to a surface of a three-dimensional map using aself-organizing map; generating a layer of dynamic informationcontextual relation map for the received unstructured text by mappingthe transformed key-phrases on to the surface of the three-dimensionalmap using the self-organizing map; forming phrase clusters for thetemplate contextual relation map; forming phrase clusters for thedynamic information contextual relation map by using the phrase clustersof the generated template contextual relation map; constructing templateand dynamic information key-phrase frequency histograms consisting ofthe frequency of occurrences of key-phrases, respectively, from thegenerated template contextual relation map and the dynamic informationcontextual relation map; and generating template and dynamic informationthree-dimensional structured maps from each of corresponding templateand dynamic key-phrase frequency histograms extracting desiredinformation by mapping the dynamic information three-dimensionalstructured map on to the template three-dimensional structured map. 9.The method of claim 8, wherein the unstructured text is received fromsources selected from the group consisting of a data base/datawarehouse, a LAN/WAN network, SAN, Internet, a voice recognition system,and a mobile/fixed phone.
 10. The method of claim 9, wherein thereceived unstructured text can be in any natural language.
 11. Acomputer implemented method, comprising: extracting multiple key-phrasesfrom unstructured text; obtaining a predetermined number of multiplekey-phrases from the multiple key-phrases; generating a layer oftemplate contextual relation map by mapping the predetermined number ofmultiple key-phrases on to a surface of a spherical map using aself-organizing map; generating a layer of dynamic informationcontextual relation map for the received unstructured text by mappingthe multiple key-phrases on to the surface of the spherical map usingthe self-organizing map; forming phrase clusters for the templatecontextual relation map; forming phrase clusters for the dynamicinformation contextual relation map by using the phrase clusters of thegenerated template contextual relation map; constructing template anddynamic information key-phrase frequency histograms consisting of thefrequency of occurrences of key-phrases, respectively, from thegenerated template contextual relation map and the dynamic informationcontextual relation map; generating template and dynamic informationthree-dimensional structured maps from each of corresponding templateand dynamic key-phrase frequency; and extracting desired information bymapping the dynamic information three-dimensional structured map on tothe template three-dimensional structured map.
 12. The computerimplemented method of claim 11, further comprising: receivingunstructured text from various text sources, wherein the text sourcesare selected from the group comprising of product manuals, maintenancemanuals, and any documents including unstructured text.
 13. The computerimplemented method of claim 11 further comprising: extracting multiplekey-phrases from the unstructured text sources; and forming the multiplekey-phrases from each of the extracted multiple key-phrases.
 14. Thecomputer implemented method of claim 13, wherein key-phrases comprise:one or more extracted key-phrases and associated preceding and followingwords adjacent to the extracted key-phrases to include contextualinformation.
 15. The computer implemented method of claim 14 whereinextracting multiple key-phrases comprises: extracting multiplekey-phrases from the unstructured text sources based on specificcriteria selected from the group comprising filtering to removing allwords comprising three or fewer letters, filtering to remove generalwords, and filtering to remove rarely used words.
 16. The computerimplemented method of claim 15, further comprising: transforming each ofthe extracted key-phrases into a unique numerical representation.
 17. Anintelligent information mining method, comprising: receivingunstructured text from various unstructured text sources; extractingmultiple key-phrases from the unstructured text; transforming each ofthe multiple key-phrases into a unique numerical representation;obtaining a predetermined number of transformed key-phrases from thetransformed multiple key-phrases; generating a first layer templatecontextual relation map by mapping the predetermined number ofkey-phrases to surface of a three-dimensional map using aself-organizing map to categorize each of the predetermined number oftransformed key-phrases based on contextual meaning; forming phraseclusters using the first layer template contextual relation map;constructing a template phrase frequency histogram consisting offrequency of occurrences of predetermined number of transformedkey-phrases from the first layer template contextual relation map;generating a three-dimensional template structured map using thetemplate phrase frequency histogram so that the generatedthree-dimensional template structured document map includes textclusters based on similarity of relationship between the formed phraseclusters; generating a dynamic information contextual relation map bymapping remaining transformed key-phrases to a three-dimensional dynamicinformation map using the self-organizing map to categorize theremaining key-phrases based on the contextual meaning; constructing adynamic information key phrase frequency histogram consisting offrequency of occurrences of remaining transformed key-phrases from thegenerated dynamic information contextual relation map; generating athree-dimensional dynamic information structured document map using thedynamic information phrase frequency histogram and the generated dynamicinformation contextual relation map which includes clusters ofinformation using the self-organizing map such that locations of theinformation in the clusters determine similarity relationship among theformed clusters; and extracting desired information by mapping thegenerated three-dimensional dynamic information structured document mapover the generated three-dimensional template structured document map.18. The method of claim 17, further comprising: extracting multiplekey-phrases from the unstructured text sources; and forming the multiplekey-phrases from each of the extracted multiple key-phrases.
 19. Themethod of claim 18, wherein extracting multiple key-phrases comprises:extracting multiple key-phrases from the unstructured text sources basedon a specific criteria selected from the group comprising, filtering toremove all words comprised of three or fewer letters, and filtering toremove rarely used words.
 20. The method of claim 19, wherein thekey-phrases can comprise: one or more key-phrases and/or one or morekey-phrases.
 21. The method of claim 20, wherein the key-phrasescomprise: one or more extracted key-phrases and associated preceding andfollowing words adjacent to the extracted key-phrases to includecontextual information.
 22. The computer implemented method of claim 11,further comprising: comparing extracted desired information to anexpected desired information to compute any error in the extracteddesired information; if the error exists based on the outcome of thecomparison, and wherein the error is due to the error in the formationof the template three dimensional structured map using fuzzy predictionalgorithm and basis histogram to extract desired information; comparingthe outcome of the extracted desired information obtained using fuzzyprediction algorithm and basis histogram to the expected desiredinformation; if the extracted desired information and the expecteddesired information are substantially same, then the dynamic informationthree-dimensional structured document map is corrected using learningvector quantization (LVQ) based negative learning error correctingalgorithm; if the extracted desired information and the expected desiredinformation are not substantially same, then the template contextualrelation map is corrected using the learning vector quantization (LVQ)based negative learning error correcting algorithm to correct thetemplate contextual relation map; extracting desired information usingcorrected template contextual relation map; comparing the extracteddesired information to the expected desired information; and if theextracted desired information is substantially different form theexpected desired information based on the outcome of the comparison,then repeating the above steps until the extracted desired informationis substantially same as the expected desire information.
 23. Thecomputer implemented method of claim 22, wherein using the LVQ basednegative learning error correcting algorithm to correct the templatethree-dimensional structured document map and the template contextualrelation map comprises: applying a substantially small negative andpositive learning correction to an outer cover of the correct andincorrect clusters in the template three-dimensional structured documentmap and the template contextual relation map using the equation:w _(j)(n+1)=w _(j)(n)−η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]w _(j)(n+1)=w _(j)(n)+η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)] whereinW_(j)=weights of node j, X(n)=input at time n, and πj,i (n)=neighborhoodfunction centered around winning node I(x).
 24. An information miningmethod, comprising: extracting multiple key-phrases from unstructuredtext; transforming each of the multiple key-phrases into a uniquenumerical representation; obtaining a predetermined number oftransformed multiple key-phrases from the multiple key-phrases;generating a layer of template contextual relation map by mapping thepredetermined number of transformed multiple key-phrases on to a surfaceof a spherical map using a self-organizing map and a gaussianapproximation neighborhood technique; generating a layer of dynamicinformation contextual relation map for the received unstructured textby mapping the transformed multiple key-phrases on to the surface of thespherical map using the self-organizing map and the gaussianapproximation neighborhood technique; forming phrase clusters for thetemplate contextual relation map; forming phrase clusters for thedynamic information contextual relation map by using the phrase clustersof the generated template contextual relation map; constructing templateand dynamic information key-phrase frequency histograms consisting ofthe frequency of occurrences of key-phrases, respectively, from thegenerated template contextual relation map and the dynamic informationcontextual relation map; generating template and dynamic informationthree-dimensional structured maps from each of corresponding templateand dynamic key-phrase frequency histograms; and extracting desiredinformation by mapping the dynamic information three-dimensionalstructured map on to the template three-dimensional structured map,respectively.
 25. The method of claim 24, further comprising: extractingmultiple key-phrases from the unstructured text sources; and forming themultiple key-phrases from each of the extracted multiple key-phrases.26. The method of claim 25, wherein extracting multiple key-phrasescomprises: extracting multiple key-phrases from the unstructured textsources based on a specific criteria selected from the group comprising,filtering to remove all words comprised of three or fewer letters, andfiltering to remove rarely used words.
 27. The method of claim 26,wherein the key-phrases can comprise: one or more key-phrases and/or oneor more key-phrases.
 28. The method of claim 26, wherein the key-phrasescomprise: one or more extracted key-phrases and associated preceding andfollowing words adjacent to the extracted key-phrases to includecontextual information.
 29. The method of claim 24, wherein the gaussianneighborhood function technique, comprises: updating values of weightvectors of winner category and neighborhood using the equation:w _(j)(n+1)=w _(j)(n)−η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)]w _(j)(n+1)=w _(j)(n)+η(n) π_(j,i(x))(n)[x(n)−w _(j)(n)] wherein=weights of node j, X(n)=input at time n, and ηn,i (n)=neighborhoodfunction centered around winning node I(x) given by the gaussiandistribution function:exp(−d ² _(1,j)/2σ(n)) wherein η(n)=the learning rate with typical range[0.1–0.01], η₀ exp(−n/τ₂), σ(n)=Standard deviation σ₀ exp(−n/τ₁),σ₀=3.14/6, and τ₁, τ_(n)=time constants, where τ₁=1000/log (σ₀) andτ_(n)1000.
 30. The method of claim 24, further comprising: calculating acumulative frequency of each category mapped to a cell in the 3Dtemplate and 3D dynamic information contextual spherical maps;calculating goodness factor for each calculated cumulative frequency;labeling each category based on the calculated goodness factor; andclustering the labeled categories using least mean square clusteringalgorithm.
 31. The method of claim 30, further comprising: if index(min(d_(m,cluster centers)))∈(i,j), then merging the clusteredcategories based on the labels, wherein m is midpoint between centers ofclusters (I,J).
 32. The method of claim 30, wherein calculating thegoodness factor comprises: calculating the goodness factor of allcategories C_(i), w.r.t each cell j using the equation:G(C _(i,j))=FClust (C _(i))/F ^(Coll)(C _(i)) wherein F^(cell) is thecategory C_(i) in relation to other categories in the cell j, F^(Coll)relates to category C_(i) to the whole collection, wherein${G\left( {C_{i},j} \right)} = \frac{{F_{j}\left( C_{i} \right)}*{F_{j}\left( C_{i} \right)}}{{{F_{j}\left( C_{i} \right)} + \sum\limits_{i}} ⊄ {A_{I}^{J}{F_{j}\left( C_{i} \right)}}}$wherein i⊂A₁ ^(j) if d(i,j)<(r₁=the radius of the neutral zone),r₁==3*d_(i,j, i,j) being adjacent andF_(j)(C_(i))=f_(j)(C_(i))/Σ_(j)f_(j)(C_(i))−the relative frequency ofcategory C_(i), wherein f_(j)(C_(i))=the frequency of category C_(i) inj.
 33. A computer-implemented system for intelligent information mining,comprising: a web server to receive unstructured text data from varioustext sources; a key-word/phrase extractor to extract multiplekey-phrases from the unstructured text data; and an analyzer totransform each extracted key-phrase into a unique numericalrepresentation such that the transformed unique numericalrepresentation; wherein the analyzer obtains a predetermined number ofextracted multiple key-phrases and generates a layer of templatecontextual relation map by mapping predetermined number of multiplekey-phrases to a three-dimensional map using a self organizing map and agaussian distribution technique to categorize the predetermined numberof multiple key-phrases based on contextual meaning; wherein theanalyzer generates a layer of dynamic information contextual relationmap by mapping the multiple key-phrases to the three-dimensional mapusing a self-organizing map and a gaussian distribution technique tocategorize the multiple key-phrases based on the contextual meaning;wherein the analyzer forms word clusters for each of the generatedcontextual relation maps, and the analyzer further constructs akey-phrase frequency histogram consisting of frequency of occurrence ofproduct and query related key-phrases, respectively from each of thegenerated contextual relation maps; and wherein the analyzer generatestemplate and dynamic information three-dimensional structured documentmaps from the constructed key-phrase frequency histogram and thegenerated template and dynamic information contextual relation mapsusing the self-organizing map and wherein the analyzer further extractsdesired information by mapping the dynamic information three-dimensionalstructured document map to template three-dimensional structureddocument map.
 34. The system of claim 33, wherein the various textsources comprise: text sources selected from the group comprisingproduct manuals, maintenance manuals, and service manuals.
 35. Themethod of claim 33, wherein the analyzer extracts multiple key-phrasesfrom the received unstructured text sources, and wherein the analyzerforms multiple key-phrases from each of the extracted multiplekey-phrases.
 36. The system of claim 35, wherein the analyzer extractsmultiple key-phrases from the unstructured text sources based onspecific criteria selected from the group consisting of filtering toremove all words comprised of three or fewer letters, and filtering toremove rarely used words.
 37. The system of claim 35, wherein thekey-phrases comprise: one or more key-phrases and/or one or morekey-phrases.
 38. The system of claim 35, wherein key-phrases comprise:one or more extracted key-phrases and associated preceding and followingwords adjacent to the extracted key-phrases to include contextualinformation.
 39. A computer-readable medium having computer executableinstruction for intelligent information mining, comprising: extractingmultiple key-phrases from unstructured text; obtaining a predeterminednumber of key-phrases from the multiple key-phrases; generating a layerof template contextual relation map by mapping the predetermined numberof key-phrases to a three-dimensional map using a self-organizing map;generating a layer of dynamic information contextual relation map forthe received unstructured text by mapping the transformed key-phrases tothe three-dimensional map using the self-organizing map; forming phraseclusters for the template contextual relation map; forming phraseclusters for the dynamic information contextual relation map by usingthe phrase clusters of the generated template contextual relation map;constructing template and dynamic information key-phrase frequencyhistograms consisting of the frequency of occurrences of key-phrases,respectively, from the generated template contextual relation map andthe dynamic information contextual relation map; generating template anddynamic information three-dimensional structured maps from each ofcorresponding template and dynamic key-phrase frequency histograms andthe contextual relation maps; and extracting desired information bymapping the dynamic information three-dimensional structured map on tothe template three-dimensional structured map.
 40. A computer system forintelligent information mining, comprising: a processor; an outputdevice; and a storage device to store instructions that are executableby the processor to perform a method of intelligent information miningfrom unstructured text data, comprising: extracting key-phrases fromunstructured text data; generating three dimensional template contextualself organized maps based on the extracted key-phrases; generating threedimensional dynamic information contextual self organized maps forinformation to be classified; and identifying desired information from acomparison of the three dimensional template maps with the dynamicinformation maps.